In tasks that require ship detection and recognition, the irregular shapes of ships and complex backgrounds pose significant challenges. This paper presents an advanced extension of the YOLOv8 model to address these challenges. A lightweight visual transformer, MobileViTSF, is proposed and combined with the YOLOv8 model. To address the loss of semantic information that arises from inconsistent scales in the detection of small ships, a layer intended for the detection of small targets is introduced to lead to improved fusion of deep and shallow features. Furthermore, the traditional convolution (Conv) blocks are replaced with GSConv blocks, and a novel GSC2f block is designed for fewer model parameters and improved detection performance. Experiments on a benchmark dataset suggest that this new model can achieve significantly improved accuracy for ship detection with fewer model parameters and a reduced model size. A comparison with several other state-of-the-art methods shows that higher accuracy can be obtained for ship detection with this model. Moreover, this new model is suitable for edge computing devices, demonstrating practical application value.

Personalized learning path considers matching symmetrical attributes from both learner and learning material. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the matching of the following symmetrical attributes of learner/material: ability level/difficulty level, learning objective/covered concept, learning style/supported learning styles, and expected learning time/required learning time. The prerequisites of material are considered constraints. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show that the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency.

In applications of software testing, the cause–effect graph method is an approach often used to design test cases by analyzing various combinations of inputs with a graphical approach. However, not all inputs have equal impacts on the results, and approaches based on exhaustive testing are generally time-consuming and laborious. As a statute-based software-testing method, combinatorial testing aims to select a small but effective number of test cases from the large space of all possible combinations of the input values for the software to be tested, and to generate a set of test cases with a high degree of coverage and high error detection capability. In this paper, the reduced ordered binary decision diagram is utilized to simplify the cause–effect graph so as to reduce the numbers of both the inputs and test cases, thereby saving the testing cost. In addition, an improved particle swarm optimization algorithm is proposed to significantly reduce the computation time needed to generate test cases. Experiments on several systems show that the proposed method can generate excellent results for test case generation.

Over the past few decades, extensive research has been conducted on software reliability growth models based on the non-homogeneous Poisson process. However, most existing studies rely on the premise of perfect debugging, failing to fully consider key factors such as potential error introduction, the diversity of failure types, and dynamic changes in the testing environment. They also neglect the systematic analysis of the testing and repair processes. This disconnection between theoretical assumptions and practical application scenarios makes it difficult for these models to accurately depict the complex phenomena in real testing processes. To address these limitations, this study proposes an integrated NHPP-based SRGM combining an imperfect debugging mechanism, the fault detection process (FDP) and fault correction process (FCP), fault heterogeneity, and change-point analysis. The model introduces dynamic correction intensity linked to pending faults, classifies faults into simple (instantly corrected) and complex (queued for FCP), and models detection and correction rates as piecewise functions before and after change points, capturing realistic scheduling logic and synchronized effects of strategy, tools, and personnel changes. On this basis, a comprehensive and optimized software release strategy is further proposed. This strategy accounts for detection costs during testing, failure repair costs, and comprehensive costs from post-release failures. Its aim is to minimize full life cycle costs while meeting the reliability targets, thus providing software project managers with a scientifically grounded and practically reliable decision-making basis leveraging the integrated modeling innovations.

With the advancement of computer science, software has become increasingly prevalent across all facets of society, making software quality issues a focal point of industry concern. The scarcity of sufficient defect data in the early stages of projects undermines prediction accuracy, driving research into cross-project software defect prediction. The traditional manual measurement features face challenges due to the data distribution discrepancies between original and cross-project contexts, which hinder the prediction effectiveness. Furthermore, single features fail to comprehensively characterize software information. This paper proposes a domain adaptation and feature fusion-based cross-project software defect prediction method (DAFF-CPDP). The model employs the TCA+ algorithm for domain adaptation and utilizes an encoder layer for progressive feature fusion. Multiple Java projects were selected for evaluation. The comparisons with various baseline models demonstrated that the proposed model outperforms both the traditional machine learning-based feature models and the diverse deep learning-based single-feature or multi-feature models. Concurrently, this paper analyzes the impact of different source projects on target projects, confirming that class-balanced datasets and datasets with smaller distribution differences are more conducive to project prediction.